1. Field of the Invention
The present invention relates to a method for generating high resolution Magnetic Resonance Imaging (hereinafter, referred to as “MRI”) image.
2. Description of the Background Art
MRI is used for measuring magnetic properties of material and producing the image thereof and there have been many studies in this field. One of conventional MRI techniques can monitor physiological information of human body tissues and magnetic resonance properties of water molecules according to the types of diseases. In line with this trend, the system performance of a MRI apparatus has gradually been improved.
Functional MRI (hereinafter, referred to as “fMRI”) is an imaging method for determining which part of the brain is activated according to time when the brain is under the stimulus of the sense of sight, auditory sense, movement or the like. The fMRI requires high temporal resolutions.
As a conventional method for obtaining this fMRI, an Echo Planar Imaging (hereinafter, referred to as “EPI”) technique with high temporal resolution has been used a lot.
The EPI technique can be used suitably when the size of an fMRI image is 64×64, but is difficult to use when the size of an fMRI image is over 64×64 due to a low signal to noise ratio (SNR). Further, the EPI technique is advantageous in that it can obtain an image quickly unlike other conventional MRI methods, such as a gradient-echo imaging and a spin-echo imaging, but is disadvantageous in that ghost artifact or geometric distortion is generated.
Further, a MRI apparatus with an intensity of 1.5 T (Teals) or 3.0 T (Tesla) has generally been used in some hospitals or research institutes and an fMRI with this degree of a magnetic field strength is also used. However, in recent years, as an MRI apparatus with a higher magnetic field is advanced, an ultra-high field system of 7.0 T (Tesla) has begun to be used. However, it is expected that fMRI photographing using the EPI technique in this ultra-high field system is impossible because the above-described problems stands out more than the low Tesla system. Accordingly, there is a need for a method of photographing a general MRI image more quickly without EPI technique.
The Generalized Series (hereinafter, referred to as “GS”) imaging technique and the parallel imaging technique, are described below with reference to the accompanying drawings.
FIG. 1 is a view illustrating a sampling method of the GS imaging technique.
The sampling method of the GS imaging technique is generally used to acquire an MRI with high temporal resolution and is a method of shortening an image acquisition time based on the fact that images in neighboring time-frames are similar to each other (refer to a reference document: Z-F. Liang and P. C. Lauterbur. An Efficient Method for Dynamic Magnetic Resonance Imaging. IEEE Transactions on Medical Imaging 1994; 13:677-686).
Referring to FIG. 1, the left-side drawing of FIG. 1 illustrates a case where magnetic resonance data is acquired by sampling it at the Nyquist rate over the whole k-space region. This method corresponds to a sampling method of a conventional imaging technique. The right-side drawing of FIG. 1 illustrates a case where magnetic resonance data is acquired by sampling it at the Nyquist rate with respect to only the low frequency region of a central portion of the whole region of the k-space. This method corresponds to a sampling method of the GS imaging technique. The term “Nyquist rate” refers to that an original signal can be recovered only when the signal is sampled at a sampling frequency twice an original signal frequency bandwidth. This minimum sampling frequency is called the Nyquist rate.
The right-side drawing of FIG. 1 shows a case where the data acquisition time is twice faster than that of the left-side drawing of FIG. 1. However, the data acquisition time can be further shortened depending on how many sampling number is set by a user.
In the sampling method of the GS imaging technique, it can be said that the high frequency component of an image is lost because only the low frequency component is sampled, but the high frequency component can be compensated for by a reference image to be obtained once for the first time by the sampling method of the conventional imaging technique. The sampling method of the GS imaging technique can shorten the image acquisition time by using the fact that images in neighboring time-frames are similar when acquiring a dynamic MRI (hereinafter, referred to as “dMRI”).
In the sampling method of the GS imaging technique, an image IGS is represented into a linear combination, assuming that the image IGS is a function as in the following equation 1. An image that is finally reconstructed becomes IGS.
                                          I            GS                    ⁡                      (            x            )                          =                              ∑            n                    ⁢                                    c              n                        ⁢                                          ϕ                n                            ⁡                              (                x                )                                                                        [                  Equation          ⁢                                          ⁢          1                ]            
where φn(x) is a basis function.
Equation 1 can be represented by a complex number sine series as in the following equation 2.
                                                        ϕ              n                        ⁡                          (              x              )                                =                                                    I                r                            ⁡                              (                x                )                                      ⁢                          ⅇ                              ⅈ2π                ⁢                                                                  ⁢                                  k                  n                                ⁢                x                                                    ,                                  ⁢                                            I              GS                        ⁡                          (              x              )                                =                                                    I                r                            ⁡                              (                x                )                                      ⁢                                          ∑                n                            ⁢                                                c                  n                                ⁢                                  ⅇ                                      ⅈ2π                    ⁢                                                                                  ⁢                                          k                      n                                        ⁢                    x                                                                                                          [                  Equation          ⁢                                          ⁢          2                ]            
where Il(x) is a reference image obtained by a general imaging method. Thus, if only cn is found, a desired image can be obtained. This can be decided by a condition that fulfills in a Fourier transform relationship such as the following equation 3.
                              Constaint          ⁢                      :                          ⁢                                  ⁢                                  ⁢                                            d              q                        ⁡                          (              m              )                                =                                    ∑              x                        ⁢                                                            I                  GS                                ⁡                                  (                  x                  )                                            ⁢                              ⅇ                                                      -                    ⅈ2π                                    ⁢                                                                          ⁢                                      k                    m                                    ⁢                  x                                                                    ⁢                                  ⁢                                                                                                                                                d                        q                                            ⁡                                              (                        m                        )                                                              =                                                                  ∑                        x                                            ⁢                                                                                                    I                            r                                                    ⁡                                                      (                            x                            )                                                                          ⁢                                                                              ∑                            n                                                    ⁢                                                                                    c                              n                                                        ⁢                                                          ⅇ                                                              ⅈ2π                                ⁢                                                                                                                                  ⁢                                                                  k                                  n                                                                ⁢                                x                                                                                      ⁢                                                          ⅇ                                                                                                -                                  ⅈ                                                                ⁢                                                                                                                                  ⁢                                2                                ⁢                                π                                ⁢                                                                                                                                  ⁢                                                                  k                                  m                                                                ⁢                                x                                                                                                                                                                                                                                                                          =                                                                  ∑                        n                                            ⁢                                                                        ∑                          x                                                ⁢                                                                              c                            n                                                    ⁢                                                      I                            r                                                    ⁢                                                      ⅇ                                                                                          -                                ⅈ                                                            ⁢                                                                                                                          ⁢                              2                              ⁢                                                              π                                ⁡                                                                  (                                                                                                            k                                      m                                                                        -                                                                          k                                      n                                                                                                        )                                                                                            ⁢                              x                                                                                                                                                                                                                                            =                                                                  ∑                        n                                            ⁢                                                                        c                          n                                                ⁢                                                  d                                                      c                            ⁡                                                          (                                                                                                k                                  m                                                                -                                                                  k                                  n                                                                                            )                                                                                                                                                                                                ⁢                                                  ∴                                          d                q                            ⁡                              (                m                )                                              =                                    ∑                              n                =                1                            M                        ⁢                                          c                n                            ⁢                                                d                  c                                ⁡                                  (                                                            k                      m                                        -                                          k                      n                                                        )                                                                                        [                  Equation          ⁢                                          ⁢          3                ]            
where dc(m) are the Fourier transformation results of Ic(x) and dq(m) is the Fourier transformation results of an image obtained by the sampling method of the GS imaging technique.
As described above, the sampling method of the GS imaging technique is a method of shortening an image acquisition time by sampling only M lines in a low frequency region of the whole k-space region. The sampling method of the GS imaging technique with respect to a one-dimensional signal has been described so far. It is, however, to be noted that this method can be applied to two-dimensional signals.
FIG. 2 is a view illustrating a sampling method of the parallel imaging technique.
FIG. 2(a) is a view illustrating a sensitivity encoding (SENSE) sampling method, which is one of parallel imaging techniques. For reference, SENSE is described in detail in a reference document (Pruessmann K P, Weiger M, Scheidegger M B, Boesiger P. Sense: sensitivity encoding for fast MRI. Magn Reson Med 1999; 42:952-962).
FIG. 2(b) shows a final MRI reconstructed by performing the Fourier transformation on magnetic resonance data obtained by the SENSE sampling method of FIG. 2(a).
In FIG. 2(a), the left-side drawing of FIG. 2(a) illustrates a case where magnetic resonance data is acquired at the Nyquist sampling distance with respect to the whole k-space region, which corresponds to the sampling method of the conventional imaging technique. The right-side drawing of FIG. 2(a) illustrates a case where magnetic resonance data is acquired at a distance twice the Nyquist sampling distance with respect to the whole k-space region, which corresponds to the sampling method of the parallel imaging technique. A data acquisition time of these methods would be twice faster than that of the conventional imaging technique. It is called reduction factor (R)=2. If the Fourier transformation is applied to the data obtained as described above, an image of a shape in which a reconstructed final MRI is folded once (2-fold) is obtained as shown in FIG. 2(b). The right-side drawing of FIG. 2(a) illustrates a case where the data acquisition time is twice fast compared with the left-side drawing of FIG. 2(a). However, the data acquisition time can be further reduced by a user depending on an image whose distance is how much greater than the Nyquist sampling distance.
In the parallel imaging technique, data is received by using a multi-channel coil such as a phased-array coil. In this case, aliasing images as many as the channel number can be obtained. Each channel has its unique property. This property decides whether the intensity of a specific portion in an image is displayed greater than the remaining portions. For example, when the number of channels is 4, top, bottom, left, and right portions of each channel have the intensity greater than that of the remaining portions. It is used as additional information for eliminating an aliasing phenomenon.
FIG. 2(b) illustrates the final MRI in which the brain of a human being is obtained as twice (R=2) the Nyquist distance by using a 4-channel coil. From FIG. 2(b), it can be seen that the images are the same, but are different in intensity distributions on a channel basis.
Images with different intensity distributions on a channel basis as described above can be modeled on the assumption that an image with uniform intensity distributions is multiplied by a component to represent the intensity. To indicate how the intensity is strong on a position basis, as described above, is called a sensitivity map. In order words, the degree of aliasing every image of each channel is dependent on the sensitivity. In this case, a case R=2 can be expressed in the following equation 4.
                                          [                                                                                S                    11                                                                                        S                    12                                                                                                                    S                    21                                                                                        S                    22                                                                                                ⋮                                                  ⋮                                                                                                  S                    41                                                                                        S                    42                                                                        ]                    ⁡                      [                                                                                U                    1                                                                                                                    U                    2                                                                        ]                          =                  [                                                                      v                  1                                                                                                      v                  2                                                                                    ⋮                                                                                      v                  4                                                              ]                                    [                  Equation          ⁢                                          ⁢          4                ]            
Where Sij is the sensitivity of an image that enters a jth aliasing term of an ith channel. In FIG. 2, j can comprise only 1 or 2 because of R=2. That is, a case where j is 1 corresponds to an original signal and a case where j is 2 corresponds to a signal serving as the aliasing term. In a similar way, Ui is a signal value of an image that enters a jth aliasing term and Vi is a value of an aliasing signal of an ith channel. This can be expressed in a simplified form as in the following equation 5.SU=V  [Equation 5]
where data obtained through an experiment is V. Thus, if only S can be calculated through an algorithm, U can be known from a matrix equation such as the following equation 6.SU=V,STSU=STV,∴U=(STS)−1STV  [Equation 6]
where S is the above-mentioned sensitivity map. It can be found by applying an appropriate polynomial fitting process to the reference image obtained by the conventional imaging technique.
The GS imaging technique and the parallel imaging technique can be applied to an ultra-high field system without ghost artifact or geometric distortion, but is disadvantageous in that a speed in which a final MRI is obtained is slow compared with the EPI technique.
FIG. 3 is a view illustrating a previous method to improve image quality using GS and parallel imaging techniques. The technique of FIG. 3 corresponds to the method proposed by Xu (refer to a reference document: Xu D, Ying L, and Liang Z P. Parallel Generalized Series MRI: Algorithm and Application to Cancer Imaging. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, 2004).
The k-space sampling method proposed by Xu is a method of sampling M lines at the Nyquist rate in the central portion of the k-space and loosely sampling them at a rate lower than the Nyquist rate in the external portions of the k-space other than the central portion. Thus, the fact that only the central portion is to be sampled at the Nyquist rate leads to the characteristic of the GS imaging technique, and the fact that only the external portions are to be sampled loosely leads to the characteristic of the parallel imaging technique.
As shown in FIG. 3, the outermost left-side drawing of FIG. 3 is a k-space sampling diagram used to obtain a reference image and also used to acquire a conventional imaging, and the right-side drawing of FIG. 3 corresponds to the sampling method proposed by Xu.
The method proposed by Xu is focused on improving the quality of an image that is further reconstructed by combining the GS imaging technique and the parallel imaging technique. This method is basically not a method of further increasing temporal resolutions. Accordingly, in this method, the quality of a reconstructed image is better compared with when using each imaging technique (the GS imaging technique or the parallel imaging technique), but is problematic in that temporal resolutions are lowered.